Evidence map›Paper›PMID 35221891›Full record

ArticleFrontiers in neuroscience2021

Level of Orthographic Knowledge Helps to Reveal Automatic Predictions in Visual Word Processing.

Zehao Huang, Shimeng Yang, Licheng Xue, Hang Yang, Yating Lv, Jing Zhao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 2 countries.

Zehao HuangJing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.
Shimeng YangJing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.
Licheng XueInstitute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China.
Hang YangFaculty of Science and Engineering, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands.
Yating LvZhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.
Jing ZhaoJing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.
Hangzhou Normal University · CNSouth China Normal University · CNUniversity of Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The brain generates predictions about visual word forms to support efficient reading. The "interactive account" suggests that the predictions in visual word processing can be strategic or automatic (non-strategic). Strategic predictions are frequently demonstrated in studies that manipulated task demands, however, few studies have investigated automatic predictions. Orthographic knowledge varies greatly among individuals and it offers a unique opportunity in revealing automatic predictions. The present study grouped the participants by level of orthographic knowledge and recorded EEGs in a non-linguistic color matching task. The visual word-selective N170 response was much stronger to pseudo than to real characters in participants with low orthographic knowledge, but not in those with high orthographic knowledge. Previous work on predictive coding has demonstrated that N170 is a good index for prediction errors, i.e., the mismatches between predictions and visual inputs. The present findings provide unambiguous evidence that automatic predictions modulate the early stage of visual word processing.

Indexed as

color matching taskEEGN170orthographic knowledgepredictive codingvisual word processing

Identifiers

PMID35221891
PMCPMC8864072
OpenAlexW4211157483

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.